Gps based location determination using accurately mapped polygonal areas

ABSTRACT

GPS-based position reporting suffers from probabilistic error. This disclosure reduces uncertainty by mapping a geometric figure that encompasses a GPS-based reported location and that bounds corresponding predetermined-as-more-probable true locations (MPTL&#39;s) among possible locations for the source of the reported location. The mapped figure is overlaid on a relatively accurate mapping of substantially adjacent polygonal areas. Areas of overlap are used for picking among the areas, the polygonal area most likely to be occupied by the source. In one embodiment, the polygonal areas are closely packed parking spots in a parking lot, garage or other like structure, the source is a customer&#39;s mobile phone and an attendant is tasked with quickly delivering ordered goods to the customer&#39;s parking spot.

CROSS REFERENCE

The disclosures of the following US applications are incorporated hereinby reference in their entireties: (1) U.S. Provisional Application No.62/453,872, filed Feb. 2, 2017; (2) U.S. patent application Ser. No.15/884,132, filed Jan. 30, 2018 originally entitled “WIRELESS LOCATORSYSTEM” and claiming priority to said Provisional Application No.62/453,872; (3) U.S. patent application Ser. No. 16/232,849 filed Dec.26, 2018 originally entitled LOCAL EPHEMERAL LOCATION TRACKING OF MOBILEDEVICE USERS; (4) U.S. patent application Ser. No. 16/265,786 filed Feb.1, 2019 originally entitled “Location Sensitive Queues Management”; and(5) U.S. patent application Ser. No. 16/725,262 filed Dec. 23, 2019originally entitled “High Confidence Isolated Presence Detection In FineResolution Region”.

BACKGROUND

There is a growing demand for customer-centric online order andappointment/reservation processing as well as to timely servicing ofon-premise customers. Providers who are asked to provide requested goodsand/or services in timely and high quality manner to online requestorsand on-premise customers and to respect appointments or reservations orexpectations of or for the same often have to cope with surges and ebbsin volume of arriving orders/appointments and variations in resources athand for satisfying customer/patron requests as well as coping withfluctuating flows of patron traffic in, out of and/or adjacent to theirestablishments. It is valuable to know when a customer arrives and wherethe customer is. Customer relations may suffer if an arriving or arrivedpatron is made to wait for unexpected long times, asked to acceptinferior servicing or has his/her order mixed up with that of another.

By way of a nonlimiting example, a fast food restaurant may offer apre-order feature where customers pre-order their desired items onlineand then drive to a designated curbside pick up spot or a designatedparking spot at which the customers can expect their ordered items to beready for immediate or almost immediate (e.g., less than 5 minute waittime) pick up. If customers are made to wait too long at the respectivepick-up areas or if their orders get mixed up with those of others, theymay become discouraged and not return to the establishment in thefuture.

It is to be understood that some concepts, ideas and problemrecognitions provided in this description of the Background may be novelrather than part of the prior art.

BRIEF SUMMARY

GPS-based position reporting suffers from probabilistic error.Uncertainty is reduced in accordance with the present disclosure bymapping a geometric figure that encompasses a GPS-based reportedlocation and that bounds corresponding predetermined-as-more-probabletrue locations (MPTL's) among possible locations for the source of thereported location. The mapped figure is overlaid on a relativelyaccurate mapping of substantially adjacent polygonal areas. Areas ofoverlap are used for picking among the areas, the polygonal area mostlikely to be occupied by the source. In one embodiment, the polygonalareas are closely packed parking spots in a parking lot, garage or otherlike structure, the source is a customer's mobile phone and an attendantis tasked with quickly and correctly delivering ordered goods to thecustomer's parking spot.

In one embodiment, there is provided a machine-implemented method ofproviding improved GPS-based determination that a specific GPS-basedposition reporting device is located in a specific one of substantiallyadjacent polygonal areas (e.g., densely packed parking spots in aparking lot, garage or other like structure). The method comprises: (a)receiving a GPS-based Location-reporting and ID-reporting Signal (GbLIS)pushed an/or pulled from a specific GPS-based position reporting deviceand extracting a respective GPS-based reported location from thereceived GbLIS; (b) positioning a mapping of the reported location on apre-established mapping of the substantially adjacent polygonal areasand generating from the on-map positioning of the reported location, anon-map bounded geometric figure (e.g., a circle) that encompasses themapped reported location and bounds on the mapping, correspondingpredetermined as more probable true locations (MPTL's) among possiblelocations for the position reporting device; (c) determining respectiveoverlaps of the on-map bounded geometric figure with respective ones ofthe mapped polygonal areas; (d) using respective overlap areas of theon-map bounded geometric figure with respective ones of the mappedpolygonal areas while excluding non-overlap areas to generate respectiveoverlap scores indicative of which of the mapped polygonal areas arelikely to encompass more of the MPTL's than others of the mappedpolygonal areas; (e) identifying a subset of the mapped polygonal areasthat have significantly higher overlap scores than others of thesubstantially adjacent polygonal areas; and reporting part or all of theidentified subset as constituting likely candidates for where thereporting device is located.

In one embodiment, the identifying of the subset of the mapped polygonalareas that have significantly higher overlap scores includes applying afilter to the generated respective overlap scores to thereby eliminatefrom further consideration, those of the generated overlap scores thatdo not satisfy predetermined significance criteria. In one embodiment,the predetermined significance criteria includes requiring therespective overlap score to be equal to or greater than a predeterminedfraction (e.g., 10%-20%) of a maximum overlap score that can begenerated for the respective polygonal area.

In one embodiment, there is provided a machine system that comprises:(a) a receiver configured to receive transmitted GbLI-signals (GPS-basedLocation-reporting and ID-reporting signals) wirelessly sourced (e.g.,via radio, optical and/or acoustic transmission) from a GPS positionreporting device disposed in a specific one of plural and substantiallyadjacent polygonal areas; (b) a digital storage configured to storetherein, respective digital representations of the GPS-based reportedlocations and specific identifications of the respective sourcesobtained from the received GbLI-signals; (c) a mapper having storedtherein a mapping of the substantially adjacent polygonal areasincluding the polygonal area occupied by the reporting device, themapper being configured to map one of the respective digitalrepresentations of the GPS-based reported locations onto the mapping andto generate therefrom an on-map bounded geometric figure thatencompasses the mapped reported location and that bounds on the mapping,corresponding predetermined as more probable true locations (MPTL's)among possible locations for the reporting device while excludingpredetermined as less probable ones among the possible locations; (d) anoverlap scores generator, operatively coupled to the mapper andconfigured to generate overlap scores respectively associated withoverlaps of the on-map bounded geometric figure with the on-maprepresentations of the substantially adjacent polygonal areas; (e) asorter, operatively coupled to the overlap scores generator andconfigured to sort the generated overlap scores according to value; and(f) a reporter, operatively coupled to the sorter and configured tooutput a report that identifies a highest scoring one or a highestscoring subset of the polygonal areas.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the Background.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are illustrated by way of example andare not limited by the accompanying figures for which like referencesindicate like elements.

FIG. 1 is a schematic diagram of an environment in which high confidencepresence detection and location determination can be desirable despiteover-time variations in GPS position reporting accuracy.

FIG. 2A is a schematic illustrating a GPS-based position reportingdevice (e.g., smart cell phone) disposed in part of a pre-mapped parkingspot.

FIG. 2B is a schematic explaining how overlap of a predetermined range(e.g., circle) for more probable true locations (MPTL's, relative to acurrently reported location) with sub-areas of accurately pre-mappedparking spots can be used for determining which accurately pre-mappedparking spot is the most likely one currently occupied by a vehiclehaving the GPS-based position reporting device.

FIG. 3 illustrates a system for determining a location of a mobile userusing a personal mobile device as a GPS position reporting device.

FIG. 4 is a flow chart of a tracking method that uses overlap of GPSprobable error range with sub-areas of pre-mapped parking spots forautomatically determining approach and final arrival to a designatedparking spot (or other accurately mapped polygonal area) by a vehiclehaving a respective GPS position reporting device.

FIG. 5 is a flow chart of a machine-implemented method for reporting outeither a best candidate among plural polygonal areas or that nopolygonal area can currently be reliably reported as being occupied by aspecific GPS position reporting device whose GbLI-signals are beingconsidered.

DETAILED DESCRIPTION

The disclosure relates to technology using resources of wirelessnetworks and personal mobile communicators to detect arrivals andrespective parked locations of expecting recipients of goods and/orservices. Some embodiments disclosed here provide improved confidencefor determined location near or within a designated parking spot (anexample of an accurately pre-mapped polygonal area) by a vehicle havinga respective GPS-based position reporting device that repeatedlytransmits (on an automatically pushed basis and/or on arequest-activated pull basis) a GPS-based Location-reporting andID-reporting Signal (hereafter “GbLIS” and also “GbLI-signal”).

Variations in signal transmission and receipt factors (e.g., noise,reflections) can lead to significant error in GPS-based locationreporting. The error between reported location (Rep'd_Loc) and truelocation (TL) of the GPS-based position reporting device can be as largeas 10 to 20 meters or even larger. In one set of embodiments, a firstCircular region Of predetermined-as More Probable Errors (CoMPE's) canbe defined as having the GPS-based position reporting device's truelocation at its center and as having within the circumscribing perimeterof that circle about 95% to 99.7% (respectively, 1-sigma, 2-sigma) ofthe more often (more probable) reported locations out of all reportedlocations as measured by sampling over a pre-specified time duration(e.g., over a 24 hour duration). The sampling-generated reportedlocations (Rep'd_Loc's) will include normal probabilistic deviations(true location plus normal errors; e.g., Gaussian distributed erroramounts and error directions) and they will also include outlierless-often (less-probable) reported locations that are excluded fromthat first circle (from the CoMPE's—not shown).

Conversely, when a GPS-based position reporting device reports itsposition, a second circular region (CoMPTL's—see briefly, FIG. 2B) canbe defined as having the reported location (Rep'd_Loc) at its center andas bounding the set of predetermined as more often (as more probable)true locations (e.g., MPTL's within the 95%-99.7% probability oroccurrence range) disposed within its circumference while outliers(anomalies) are excluded. Each instance of a predetermined true locationequals reported location minus the associated measured error for thatsampling-generated reported location and for that time of report. It iswithin the contemplation of the present disclosure to use less stringentencapsulation of the more probable reported locations such as the moreprobable 68% (1 sigma on the Gaussian distribution graph) of allstatistical samplings. The encapsulation of the predetermined-as-moreprobable reported locations should contain at least more than 50% of theclosest sampling-generated reported locations (Rep'd_Loc's) relative tothe corresponding true location (TL). In other words:True_Loc=Rep'd_Loc−Reporting_Error and the predetermined as MoreProbable True Locations (MPTL's) are the greater than 50% of the closestof the statistical samplings for the given true location (TL) as takenover the predetermined time duration (e.g., 72 hour sampling window).The error (Err) in the GPS-based reported location (Rep'd_Loc) can bedue to one or more factors such as for example, interference fromambient noise, wave distortion from current weather conditions (e.g.,rain), the current disposition of a constellation of in-line-of-sightGPS satellites relative to the GPS-based position reporting deviceand/or due to reflected GPS signals.

The associated, relatively wide and current Circle of predetermined asMore Probable True Locations (CoMPTL's, e.g., encompassing circle thatcan have a radius of 10-20 meters or larger as measured from thereported location (Rep'd_Loc)) can lead to significant uncertainty aboutwhere the GPS position reporting device (e.g., 215′ of FIG. 2B) is trulylocated when it is not possible to see that reporting device. Ideally,the provider of requested goods and/or services would like to timelydeliver the customer's requested goods/services to the correct parkingposition (or other pre-mapped polygonal area) almost immediately afterthe customer has arrived and without calling the customer andembarrassingly asking, “Where are you?”. But given a CoMPTL's circlethat can reasonably have a radius of 10-20 meters or larger and giventhe normal width of a conventional parking spot being about 2.65 meters(average minimum width in the U.S.A.), the true location (True_Loc or TLfor short) can be any of about 15 parking spots (assuming cars can parkclose together in a head to head as well as close side by sideconfiguration) this can be a problem. When the parking lot is crowded,the provider may be left reasonably unsure as to which parking spot isthe correct one to be receiving the pre-ordered goods/services. Theprovider might try to call the recipient's phone and ask, “Where areyou?”. However, it can be problematic for a customer to explain whereexactly he or she is when in an unfamiliar parking lot, garage or othersuch structure. Moreover, the customer might be talking at the time withsomeone else on his/her phone and the provider will just get a busysignal. A better solution would be desirable.

In one embodiment, a mobile wireless device normally or routinelycarried by the recipient (e.g., the recipient's smartphone, smart watchor other such routinely carried or worn mobile communication device) isused as a remote GPS-based position reporting device (and optionallyalso as a velocity reporting device) for keeping track of the locationof the recipient (and optionally for also keeping track of the velocityof the recipient). In particular, the mobile wireless device is used todetermine better than 50% likelihood of presence and stoppage of therecipient in an accurately pre-mapped polygonal area such as a specificparking spot. The GPS-based position reporting device automaticallytransmits on an automatically pushed and/or request-pulled basis, aGPS-based Location-reporting and ID-reporting radio Signal (“GbLIS”,also denoted herein as “GbLI-signal”) that provides a current locationreport based on GPS position determining and also provides a uniqueidentification of the reporting device and/or a unique identification ofthe possessor of the device and/or of a transaction associated with thedevice or its possessor. Optionally, the GbLI-signal may also regularlyor from time to time include velocity or acceleration reportinginformation (e.g., based on an in-device accelerometer). In oneembodiment, the wirelessly transmitted GbLIS's are output in response towireless request signals for the respective GbLIS's (pull signals) assent from the report receiving and analyzing system. In the same or analternate embodiment, the wirelessly transmitted GbLIS's areautomatically output as push signals without need for request signalsfrom the report receiving and analyzing system.

To compensate for uncertainty as to where the true location (TL) isbecause of the size of the CoMPTL's circle currently surrounding thereported location, a list of Even-More Probable True Locations (EMPTL's)is generated by overlapping a mapping of the CoMPTL's circle on top of amapping of accurately located polygonal areas that are known to be trueparking spots. The on-map overlap area in each polygonal area is takenas representative of the relative probability that the recipient isparked in that parking spot. The listing of EMPTL's (Even-More ProbableTrue Locations) is numerically sorted to identify the parking spot (orother polygonal area) having the highest relative probability and thenthe next highest relative probability and so on. In essence, those ofthe more-probable true locations (MTL's) that place the recipient's GPSposition reporting device outside of true parking spots are taken out ofthe equation. EMPTL's=All_MPTL's minus MPTL's_Not_Inside_A_Polygon. Thiselimination of unreasonable MPTL's improves the chance that only themore reasonable ones of predetermined-as-probable true locations (betterMPTL's) will be listed. The listing can be combined with other recordedinformation (e.g., the vehicle's probable trajectory shortly beforestopping in a parking spot) to increase the likelihood that the correctparking spot will be identified as the most probable one where therecipient is waiting for his/her pre-ordered goods/services. In oneembodiment, a filter is applied to an initial listing of the EMPTL's byremoving those overlaps that fail to satisfy a minimum overlap (orminimum overlap score) criteria. For example, in one embodiment, theminimum overlap is at least 10% to 20% of the respective polygonal area.

It is to be understood that FIG. 2B assumes a circular region ofsame-weighted, more probable true locations (CoMPTL's 218) whenexplaining an example of the method. However, it is within thecontemplation of the present disclosure to instead use non-circularclosed-boundary geometric figures (e.g., ellipses, hexagons, octagons orother polygons) that are geographically oriented to account forpredetermined contextual aspects of the associated parking lot, garageor other like structure; for example how the lot orients relative totrajectories of in the sky non-geostationary GPS satellites.

It is also within the contemplation of the present disclosure to not usesame weights (meaning equal probability of occurring) for all the MPTL'swithin the circle (e.g., CoMPTL's 218) or within thealternatively-shaped bounding geometric figure for the correspondinglybounded MPTL's and to instead use a probability-of-MPTL distributionfunction other than a level one within the bounding geometric figure.For example, the circle (CoMPTL's 218) could be subdivided into annularsections (and a central smaller circle) each having a respective weightfactor. The innermost part of the circle (next to its center) may havethe greatest weight factor and then annular sections radially furtherout from the center may be assigned successively lower weight factors.The areas of overlap (described below) are then calculated using theweight factors to thus give the radially more inward of the logicalMPTL's more weight than the radially outward MPTL's. In other words,respective overlap scores are assigned to respective ones of thepolygonal areas (e.g., mapped parking spots) based on integrating therespective weighting factors with the respective overlaps of theirrespective annular sections. The probability distribution function forthe MPTL's can be any kind other than the given example of annularsections. More specifically, the probability distribution functionwithin the circle or other geometric shape bounding the MPTL's canaccount for orientation factors such as how the lot orients relative tocurrent in the sky positions of non-geostationary GPS satellites, howGPS signals bounce off of adjacent structures and/or other such factors.More generally, the shape of the bounding geometric figure and/or theshape of the probability distribution function (weighting function) usedwithin it can vary with extant context (e.g., the reported location, thecurrent weather conditions, current level of measured ambient noise,presence of surrounding radio wave reflecting structures, time of day,day of year, and so on).

While one example given here relates to the fast food pre-ordering andcurbside pick-up industry, the present teachings are not to be limitedto just such an example. There are many aspects of day-to-day livingwhere appointment-makers, order-placers and/or prospective recipients ofgoods/services expect to have their presence in a designated wait-at andpick-up location (or service delivery location, e.g., valet parking)properly noted and corresponding goods/services timely provided, forexample at a scheduled time or in a scheduled or implied time span afterarrival. The respective recipients/appointment-makers may experiencedissatisfaction and disappointment if: (a) their expectations are notwell managed, (b) if queues for different kinds of patrons (e.g.,drive-through ones, pick-up-at spot ones) are not well managed and waittimes are substantially longer than planned for, (c) if goods/servicesprovisioning resources are not well managed to coincide withexpectations and arrival times of recipients and (d) if the requestedgoods/services are not provided in timely, high quality manner or not atall. Further examples of where similar kinds of issues typically ariseinclude slow-food restaurants that feature a to-go pick up service atcurbside or in the parking lot, garage or other like structure. Yetother examples include medical or alike service providing venues wherepatients drive up to a parking area adjacent to an urgent care centerwith the expectation that an urgent care health provider will meet themthere (e.g., with a wheel chair or gurney) to immediately attend totheir needs. Entertainment providing venues may have similar problemswhere customers pre-order tickets online, show up at avalet-provisioning spot and find no valet parking attendant there topark their car. Yet further examples include item-pick up areas wherepatrons have made appointments to pick up online pre-ordered goods,timely show up at the agreed to pick up area and then have to wait forunreasonably long wait times because service personnel are unaware oftheir presence at a specific parking spot.

For sake of brevity, “goods/services” will be used herein to refer tothe provisioning of any one or more of goods, services and serviceproviders as appropriate for a given context.

In accordance with the present disclosure, a more reliable determinationis arrived at with respect to which parking spot or other polygonal areaa specific GPS position reporting device is located in by having apredetermined and relatively accurate mapping of the parking lot, garageor other like structure and mapping the GPS-based reported location ontothe same map. Then a mapping of the CoMPTL's circle (or otherappropriate geometric bounding figure representative of its bounded moreprobable true locations) is overlaid on the map and overlap areas orassociated overlap scores are computed for where the polygonal areasoverlap with the CoMPTL's circle (or other appropriate geometricbounding figure). The polygonal area that receives the highest (andpreferably above-minimum threshold score) overlap score is deemed to bethe most likely candidate for where the GPS position reporting device islikely to be situated. Further details are provided below.

It is understood that the present subject matter may be embodied in manydifferent forms and should not be construed as being limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this subject matter will be thorough and complete and will fullyconvey the disclosure to those skilled in the art. Indeed, the subjectmatter is intended to cover alternatives, modifications and equivalentsof these embodiments, which are included within the scope and spirit ofthe present teachings. Furthermore, in the following detaileddescription of the present subject matter, numerous specific details areset forth in order to provide a thorough understanding of the presentsubject matter. However, it will be clear to those of ordinary skill inthe art that the present subject matter may be practiced without suchspecific details.

More specifically, when the term “GbLIS” (GPS-based Location-reportingand ID-reporting radio Signal) or its equivalent “GbLI-signal” is usedherein, it is to be construed broadly unless otherwise specified asencompassing any wirelessly transmitted signal (or set of signals) thatcan be detected as specifically indicating presence of its emitter in arelevant finite area and providing information about the GPS-baseddetermination of location of that GbLIS-emitter as well as uniquelyidentifying the emitter and/or uniquely identifying the possessor of theemitter or a transaction associated with it. Optionally, as noted abovethe GbLIS-signal(s) may regularly or from time to time provide currentvelocity information. In the case of automatically pushed GbLIS's, arepeatedly broadcast Bluetooth beacon (modified to include the GPSinformation) can qualify as a GbLIS. Repeatedly broadcast beacons ofother types of radio protocols including spectrum hopping ones canqualify as pushed GbLIS's. A repeatedly broadcast Wi-Fi signal or UWBsignal can qualify as a GbLIS. The repeated broadcasting of the GbLIScan be due to built functions of the utilized operating system in theassociated mobile device or due to specially installed programs,applications or services (including Background Peripheral Services, orBPS's). In one embodiment, a BPS is configured to respond to a pullsignal wirelessly sent from a controlling system, to awaken in responseto the pull signal, to then send out the GbLI-signal and afterwards goback to sleep, thus saving battery power. The GbLIS may contain anidentification that uniquely identifies its mobile device and/oruniquely identifies its user and/or uniquely identifies a specifictransaction involving corresponding goods and/or services as well ascontaining the GPS-based current location report and optional currentvelocity report.

FIG. 1 illustrates an environment (e.g., a fast food retail venue) 100which can support a number of different online and in-person orderingschemes in accordance with the present disclosure including curbsideand/or parking lot pickup. The schematic illustration depicts the venue100 as having venue-controlled or venue-monitorable areas such as avehicle drive-through interface area 110, a pedestrian interface area120, a product processing and delivering area 130 (e.g., kitchen 131 andattendants' interface area 132), a vehicle parking area 140 and/or othervehicle accommodating areas (e.g., a storefront at curb parking area—notshown). For some of relatively small regions among these areas, it canbe important and financially practical for business operations to detectwith a high degree of resolution, isolation and confidence the presenceof a single beacon transmitter in that relatively small region (narrowwidth region of interest). An example of such a fine resolution regionof interest is that adjacent to the verbal order receiving microphone orspeaker of a fast food drive through area. Another example of such afine resolution region of interest is that adjacent to the order pickupwindow 112 of a fast food drive through area. Particularly whencustomers are tightly queued one immediately next to the other it isimportant that taking of orders and corresponding handing out of orderedgoods should not be mixed up when using the beacon emitters of customersas a means for associating the orders with the respective customers.

However, when it comes to outdoor parking lots, large garage structures,long stretch curbside parking spots and the like (represented by item140 in FIG. 1), it is often not financially practical for businessoperations to implement position detectors having fine resolution (e.g.,determining location to within a meter or even less). Instead, it ispreferable to rely on course position determining technologies suchGPS-based position determination and/or triangulation between cellularbase stations (e.g., cell towers, 5G stations). These courser positiondetermining technologies can generally determine location as beingsomewhere in areas of a radius of many meters. More specifically, asnoted, GPS-based position determination typically has a relatively largeCircle of predetermined-as-More Probable True Locations (CoMPTL's) inthe range of radii of 10 meters or more in length. That can beinsufficient for pinpointing the parking location of a vehicle parked inan outdoor parking lot or the like 140. The present disclosure providesa method and mechanism for improving on that by using pre-mapped highaccuracy locations of certain polygonal areas (e.g., staked out parkingspots).

The above are examples of exterior goods/services providing spots thatmay be found in a patrons-servicing establishment. The illustratedexample shows that an establishment 100 may include interiorgoods/services providing spots an interior sitting area (e.g., a waitingarea with furniture such as metallic tables and/or metallic chairs)where patrons such as 123 who have pre-ordered online or at a verbalorder taking station while using worn (or carried) location-revealingmobile devices 115′ may wait for the requested items to be delivered tothem (to their specific seat) by a service provider (e.g., waiter) or tobe notified that the requested items are available at a pick-up windowor counter. The establishment may include interior queue lines whereother patrons who have pre-ordered online (and who optionally carry orwear location-revealing mobile devices or who plan to verbally order atthe counter, can line up for more immediate service at a service counterwhere; when they reach the counter, they expect to immediately receivetheir preordered items (e.g., fresh hot food items, cold drinks) or havetheir verbal order immediately taken at that spot. The establishment mayinclude one or more drive-through servicing stations and/or windows 112(optionally of different kinds, including those with order receivingmicrophones or speakers) to which driving-through vehicles 114 approachin order to verbally place orders or immediately receive delivery oftheir online or otherwise pre-ordered (and optionally pre-paid for)items. The driving-through vehicles 114 and/or their drivers may possesslocation-revealing mobile devices such as smart phones 115, smartwatches and so on used for associating the orders with uniqueidentifications broadcast by their location-revealing mobile devices(beacon emitting devices) and for thus also associating the orders withthe respective customers (and for optionally pre-paying for thegoods/services) and for tracking fine resolution locations of thelocation-revealing mobile devices so that the ordered goods/services canbe correctly provided to the associated customers as they approach theinterior or exterior wall (e.g., 112) servicing stations.

In one embodiment, the establishment 100 includes or has access tomedium and fine resolution location determining scanners (e.g.,Bluetooth™ scanners, Fifth generation (5G) Wi-Fi scanners, etc.) 127which cooperatively interact with software installed in thelocation-revealing mobile devices (e.g., 115, 115′, 115″, 115′″) of thevarious patrons (e.g., seated patron 123, driver in vehicle 114) fordetermining to varying degrees of resolution, their respective locationsin areas or sub-areas (e.g., 110, 120, 140, 160) for which theestablishment has tracking rights or permissions where the tracking canbe carried out to respective levels of course, medium and finerresolutions (e.g., the finer being to within about a meter or less andthe course ones having resolutions of no better than 5 to 10 meterradii). Although not shown in FIG. 1, it is to be understood that thefine resolution location determining scanners 127 are operativelycoupled (e.g., wirelessly or by way of cables or IR light beams) to acomputer network for relaying location determinations made by them forprocessing by one or more data processors available on the network. Thescanners 127 can keep track to appropriate levels of resolution (e.g.,to within 6 feet, 3 feet or less, etc.) of where pedestrians are in thepedestrian interface area 120 and where vehicles are in vehicleaccommodating areas such as drive-through lane 110. For the case oflarger scale areas such as parking area 140, coarser locationdeterminations technologies are preferably used as shall be detailedherein. Although shown in FIG. 1 only by means of schematic dots, it isalso to be understood that marked off parking spots 141 a-141 n (nrepresenting a relatively large number; e.g., 10, 20 etc.), respectiveGPS position reporting devices 115′″ may be transmitting GbLI-signalsfrom within those parking spots 141 a-141 n after the respectivevehicles have parked there. Some parking spots may be empty. It is to beunderstood that although Bluetooth™ and 5G directed beam Wi-Fitransceivers are mentioned as examples of medium and fine resolutionlocation determining scanners (e.g., ones that can also receivecorresponding GbLI-signals), the present disclosure is not limited tojust these examples. Rather, numerous alternative wireless devices canbe used for locating patrons to different levels of resolution (e.g.,from coarse ones having 20 meter or greater radii CoMPTL's to finerresolution ones of one meter or less, preferably 2 feet or less), theseincluding optical (e.g., IR) receivers of reporting signals, radioreceivers and/or acoustic signal receivers. In particular, in thisdisclosure, the use of the coarser position reporting devices such asthose based on GPS position determinations will be discussed.

In one embodiment, it is desirable to have a GPS-based positionreporting feature of each patron's respective mobile device (e.g., 115″)active and reporting the location of the patron's vehicle (e.g., 114″)even before the vehicle enters (e.g., via entrance way 161 from approachstreet 160) the associated parking lot, garage or other like structure140 having marked off parking spots (e.g., 141 a-141 n). The GPS-basedposition reporting feature outputs GbLI-signals to corresponding reportreceiving detectors of the establishment 100. A mapping of the markedoff parking spots (e.g., 141 a-141 n) and optionally of other marked offpolygonal areas in the parking lot, garage or other like structure 140(e.g., no-parking areas, crosswalks) is used in conjunction with thereceived GbLI-signals to improve resolution in the determinations ofwhich polygonal area (e.g., parking spot) the vehicle 114″ is mostlikely to be located.

Referring to FIG. 2A for purpose of an introductory context 201, in someinstances a patron is instructed to park his/her vehicle 214 in anexterior parking spot 242 (exterior of the main interior portions of theestablishment (e.g., 120, 130)) and informed that the ordered orrequested (and optionally pre-paid for) items will be immediately orshortly thereafter (e.g., within 5 minutes) be delivered to him/her atthat pickup spot. In accordance with the present disclosure the pickupspot 242 is a polygonal area, for example a rectangular area having anaccurately pre-mapped width boundary 242 w and length boundary 242L. Thepolygonal area does not have to be rectangular. It can have any of manyother closed boundary shapes such as trapezoidal, triangular and so onand the sizes of the polygonal areas can vary across the parking lot,garage or other like structure. This offered service feature ofdelivering to the parked location creates several problems for thegoods/services provider. For example, the provider has to figure out howto make sure the delivered goods (e.g., food) are in ripe condition fordelivery at the appointed pickup spot 242 (e.g., fresh and hot), how tomake sure a parking lot attendant (e.g., from interface are 132 ofFIG. 1) is ready to get the ordered or requested items and deliver themon time to the appointed pickup spot 242 and how to inform the parkinglot attendant as to which of the many spots (e.g., 141 a-141 n) in theparking lot, garage or other like structure 140 is the one that thispatron is parked in. An additional problem is how to allow the waitingrecipient to use his mobile device while waiting. Yet another additionalproblem is how to do all this while keeping costs low.

The illustrated introductory context 201 of FIG. 2A is somewhatsimplistic because no vehicle (e.g., motorcycle, car, bus, truck) shouldreasonably park to the right of parking spot 242, say over the raisedcurb 248. Moreover, in the illustrated context 201, no additionalvehicle can park in an area immediately in front of vehicle 214. Theparking angle is at 90° to the direction of the parking row. However, itis to be understood that in other contexts, the parking lot, garage orother like structure 140 may be crowded and have areas that allow forangled parking with vehicles facing head on to each other as well asbeing side-to-side with one another. Parking spots may come in variedsizes (e.g., compact, full size, extra large) and different shapes. Thelots may have parking spots with different parking permission rules(e.g., disabled, reserved, electric only, motorcycles only, 5 minutepickup only, and so on). Additionally, FIG. 2A shows a bullseye targetmarking 246 painted on the floor. Possible uses for this marking 246will be described later.

Referring to FIG. 2B, shown is a second context 202 which represents areal world situation that includes the illustrated GPS satellites271-274, parking spots 241-244 and the illustrated data processingcircuits 281-285 and simultaneously represents a mapping of the parkingspots 241-244 that has overlaid on it a corresponding mapping of ageometric figure (e.g., circle 218) which bounds on-map areas that cancontain those of possible true locations of a GPS position reportingdevice 215′ the locations that had been predetermined as being the moreprobable true locations (MPTL's) for that device relative to a location217 reported by that device. More specifically, in FIG. 2B, firstthrough fourth substantially adjacent rectangles 241, 242′, 243 and 244represent respective allowed parking spots of different sizes andorientations. The boundaries of the parking spots (241-244) have beenpre-established for example through surveying with appropriate means(e.g., laser measurements taken relative to a surveyor's medallion whichcould be a function of marking 246 of FIG. 2A) such that the locationsof these boundaries are accurately known to a spatial resolution muchfiner than possible with a single GPS reading (the latter being prone tothe above-described probability of error (PErr) issues). The representedmapping of the situation need not extend much beyond the size of largestCoMPTL's (Circle of predetermined as More Probable True Locations)possible for the given situation. The meaning of parking spots (or othersuch polygonal areas) being substantially adjacent to one another canvary based on context but generally means that the parkingspots/polygonal areas are spatially packed next to one another such thatthe largest CoMPTL's will encompass two or more of those parkingspots/polygonal areas.

In the illustrated example 202, the reported location 217 that isdetermined by device 215′ based on the current in-view GPS satellites271-274 (and in presence of ambient noise 279 and/or other errorcreating factors) ends up being located somewhere in a no-parkinginter-space 249 between substantially adjacent first and second parkingspots 241 and 242′. Polygonal areas 243 and 244 are further parkingspots disposed within the vicinity. It is to be understood that FIG. 2Bis not necessarily to scale. As mentioned earlier, a typical CoMPTL's(Circle of predetermined as More Probable True Locations) may have aradius that covers many more parking spots but it doesn't have to bethat large. Its size can vary based on extant conditions. For sake ofsimplicity, the illustrated CoMPTL's circle 218 is shown only partiallycovering first and second polygonal areas 241 and 242′ as well as theinterposed no-park zone 249 (e.g., driver walkout path). The problemhere is to decide while not being able to see GPS position reportingdevice 215′ whether its true location (TL) 216 is inside the firstpolygonal area 241 or inside the second polygonal area 242′.

In accordance with the present disclosure, overlap areas (e.g., OA1 andOA2) are automatically determined for the CoMPTL's 218 as overlaid on asame scaled mapping of the polygonal areas 241-244. If OA2 is determinedto be sufficiently greater than OA1 (e.g., at least 10% greater) then anautomated decision is made that the position reporting device 215′ ismore likely inside polygonal area 242′. Alternatively, if OA1 isdetermined to be sufficiently greater than OA2 then an automateddecision is made that the position reporting device 215′ is more likelyinside polygonal area 241. In one variation the CoMPTL's 218 issubdivided into an inner smaller circle and surrounding annular ringslike 218 a. Other forms of subdivision are possible. Each subdivision isgiven a weight, say W1 for the inner small circle, W2 for annular ring218 a, W3 for the next radially outward ring and so on, where in oneexample, W1>W2>W> . . . ≥1. (In an alternate embodiment, W1<W2>W3> . . .≥1. In yet another alternate embodiment, one or more of the weights areequal to one or less than one.) An effective overlap score for secondpolygonal area 242′ is then computed as W1 times that portion of theinner small circle overlapping polygon 242′ plus W2 times that portionof annular ring 218 a overlapping plus W3 times the overlap by the nextradially outward ring (not shown) and so on. A same similar calculationwill be performed for the respective effective overlap score for firstpolygonal area 241. As will be appreciated, shifting the reportedlocation 217 slightly to the right along the Y direction may favorpolygon 242′ as having the greater overlap area OA2 (or alternativelythe greater effective overlap score) while shifting the reportedlocation 217 slightly to the left along the Y direction may favorpolygon 241 as having the greater overlap area OA1 (or alternatively thegreater effective overlap score). Moreover, shifting the reportedlocation 217 significantly to the back along the X direction may bringfourth polygon 244 into play as a candidate parking spot inside of whichthe position reporting device 215′ is likely to be located. It is to beunderstood that calculation of unweighted overlaps or overlap scoresdoes not have to be to extreme precision. Approximating trapezoidsand/or triangles can be used to approximate the shape of the circle (orother such bounding geometric figure) and its subdivisions. This allowsfor reduction of computational workload. The term “overlap score” can bedeemed synonymous with unweighted overlap area or its relativeequivalent when the weights (e.g., W1, W2, W3, etc.) all equal one oranother constant.

In one embodiment, after the vehicle is determined to be parked becauseits speed is zero for a predetermined duration (based on accelerometerreadings and or lack of change over time in the reported location); thesize (e.g., radius) of the CoMPTL's 218 is determined based on currentlypresent or normally present ambient noise 279 and/or based on otherfactors (e.g., time of day, day of year) that can affect probable sizeand/or probable direction of error as between true location (216) andreported locations. Optionally the CoMPTL's 218 is subdivided andrespective weights (e.g., W2) are assigned to the subdivisions (e.g.,218 a). The corresponding overlap area (e.g., OA1, OA2) or effectiveoverlap score is computed (e.g., approximated) for each of therespective polygonal areas (e.g., 241-244) in the broader area. Then theresults are sorted with the greatest calculated overlap area (e.g., OA2)or the greatest calculated overlap score being at the top of the list.In one embodiment, a filtering step is applied to the sorted list.Computed overlaps or overlap scores that are less than a predeterminedfraction of the maximum overlap computation possible for each respectivepolygonal area (say less than at least 10% to 30% of the possiblemaximum) are automatically deleted from the list. If none of thecomputations remain on the list after this filtering step then theparking attendant (from area 132 of FIG. 1) is automatically informedthat a different method of locating the customer needs to be used (e.g.,trying to call the customer). If only one computation remains on thelist after this filtering step then that customer location isautomatically reported to the attendant as the location to be served. Iftwo or more computations remain on the sorted list after this filteringstep then the top N computations (e.g., the top 3) are automaticallyreported to the attendant as parking spots to try in that order.

In FIG. 2B, item 281 represents a wireless receiver that receives theGbLI-signals that are automatically repeatedly transmitted from theposition reporting device 215′. The receiver 281 extracts the reportedlocation information (217), the unique identification of the transmitter(and/or of the user or transaction) and optionally, velocity informationthat may be present in the received GbLI-signals. It adds a time stamp(time of receipt) to the extracted information and then forwards adigitized version of the same to a database (DB) 282 for storage andfurther processing.

Item 283 represents a data processing circuit (mapper) that has storedin it a relatively accurate map of the polygonal areas in the vicinity(e.g., that which includes the reported location and area around it atleast to spatial size of a maximum possible CoMPTL's). As processingbandwidth allows, the mapper scans the DB 282, pulls out the latestreported location information (217) and optionally any prior trajectoryinformation associated with the latest reported location information.The latest reported location is mapped onto the pre-stored map of thevicinity and then the mapper 283 generates from the on-map positioningof the reported location, an on-map bounded geometric figure (e.g.,CoMPTL's 218) that encompasses the mapped reported location and boundson the mapping, corresponding predetermined as more probable truelocations (MPTL's; e.g., the more probable 95%) among possible locationsfor the position reporting device while excluding the less probable truelocations (e.g., the less probable remaining 5%). The mapper thenidentifies the on-map overlapped areas (e.g., OA1 and OA2) for thegenerated on-map bounded geometric figure (e.g., CoMPTL's 218) and theon-map representations of the local polygonal areas (e.g., 241-244). Itis to be understood that the division between the DB 282 and the mapper283 is a variable one since the mapping of the local vicinity can bepartly or fully contained in the DB 282 rather than in the mapper.Therefore when the above mentions the mapper 283 as having a mapping ofthe local vicinity, the latter could have been obtained from the DBrather than being always stored in the mapper 283.

Item 284 represents a data processing circuit that receives digitalrepresentations of the mapped polygonal areas (e.g., 241-244) and of themapped geometric figure (e.g., CoMPTL's 218), automatically determinesthe relative amounts of overlap for the identified overlaps andgenerates corresponding overlap scores. Item 285 represents a dataprocessing circuit that receives the overlap scores generated by circuit284 and the associated identifications of the overlapped polygonalareas. Circuit 285 then optionally filters the received overlap scores(e.g., to enforce a minimum overlap sufficiency requirement ifapplicable), sorts them and then outputs a report as to which polygonalarea or areas are the best candidates; or if none, it reports that noarea could be reliably picked by this method. The user can then choosealternate methods for determining the location of the GPS positionreporting device (e.g., 215′).

Additional factors may be used to improve the chance that the parkingdelivery attendant (e.g., from area 132) will pick the right parkingspot and right vehicle. For example, based on the unique identificationin the GbLI-signal, a customer profile may exist that includesinformation about the customer's normally used vehicle such as type,make, model, license number, color and so on. That additionalinformation is pulled and forwarded (e.g., wirelessly) to the attendantwho is servicing the curbside or like delivery spot and the attendantuses the information in combination with the listing of most likelycandidate spots to pick the most likely vehicles in order of theirlisted likelihood.

Alternatively or additionally, in one embodiment, the trajectory of thevehicle (e.g., 114″ of FIG. 1) is automatically monitored after itsspeed falls below a predetermined first value (e.g., below 35 Miles PerHour) and remains above a predetermined second value (e.g., above 5 MPH)under the assumption that the driver is looking for a parking spot topull into. During this time, rather than trying to determine whichparking spot the vehicle is stopped in, the GPS-based reported locationsextracted form the received GbLI-signals are used to approximate whichapproach lane in the parking lot, garage or other like structure 140 thevehicle is likely to be moving along. Short pauses (e.g., 15 seconds orless) for stop signs and the like are ignored. In one embodiment, thepre-parking trajectory information is relayed to the assigned deliveryattendant (of area 132, and optionally to the kitchen 131) to informhim/her (and/or the kitchen) that the customer is in the process oftrying to find a spot to park the vehicle in. After the vehicle stopsfor more than a short pause (e.g., stops for 30 seconds or more), it isdetermined that the vehicle has likely parked. Then the above, zerospeed process is used to determine which parking spot is the most likelyone. However, because the trajectory of the vehicle just prior toparking is known, that information can be used in combination with thesorted list of likely parking spots (or other such polygonal areas) toimprove the chance of picking the right parking spot. For example, basedon the approach trajectory prior to full stoppage, some candidateparking spots (or alike polygonal areas) can be excluded from thecandidates list (the sorted list of overlaps). It is to be understoodthat the area overlap method by itself is usable irrespective of whatthe different shapes and sizes of the polygonal areas are. However, sizeand shape of parking spot may be taken into consideration when make,model and other profile extracted details about the vehicle and itsowner are obtained. For example, larger sized vehicles may notreasonably fit into smaller parking spots and thus the latter may befiltered out from consideration. Owners who are not entitled to park inspecial privilege parking spots should not be doing so and thus thelatter may be filtered out from consideration as reasonable candidates.

Referring back to FIG. 2A, in one embodiment, pre-marked target areaslike 246 are distributed about the lot and used for taking samplings ofcurrent GPS-based reported locations while the position reporting deviceis located directly above the center of the target 246. Statisticalprofiles are produced over appropriate time durations (e.g., a 24 hourday, a 72 hour sampling window, week, month, etc.) for determiningexamples of more probable and less probable reported locations at eachtarget position 246 in the parking lot, garage or other like structure140. (Only one target is shown in FIG. 2A but there can be moredistributed about the lot.) If later, a reported location (e.g., 217) isdisposed much further away from any pre-marked target area (e.g., 246)extrapolation between the CoMPTL's circles or alike MPTL's-boundinggeometric figure may be used to derive the appropriate MPTL's-boundinggeometric figure for that particular reported location (e.g., 217).

In one embodiment, the samplings for deriving appropriateMPTL's-bounding geometric figures are taken using a remotely controlledflying drone (not shown) having a GPS receiver at its top side, a cameradisposed underneath for spotting the center of the target 246 from itsunder carriage and a wireless transceiver operatively coupled to the GPSreceiver and configured to automatically repeatedly output testGbLI-signals for verifying operability of the system and developing thestatistical models for the more probable true locations when given theextant conditions (e.g., time of day, weather conditions, reflectivesurrounds, etc.) at the respective reported locations. Alternatively oradditionally, a parking lot attendant can periodically walk about thelot with a pole having the GPS receiver at its top (at a predeterminedheight). The attendant can spot the bottom of the pole at the center ofthe known targets 246 (only one shown) for taking the statisticalsamplings. The attendant may carry the wireless transceiver thatautomatically repeatedly outputs the test GbLI-signals or it may bemounted on the pole and operatively coupled to the GPS receiver. Thuscontext appropriate CoMPTL's (or other appropriate MPTL's-boundinggeometric figures) may be developed for each unique parking lot, garageor other like structure 140. Alternatively, the CoMPTL's may beselectively picked from sets developed at substantially similarstandardized lots.

Referring to FIG. 3, illustrated is a system 300 configured for managinglocation sensitive queues and wait-lists, including managing delivery ofgoods/services to vehicles (e.g., 362) approaching and parking intocurbside or alike pickup spots. The system 300 includes portions forautomatically determining both coarser and pinpointed respectivelocations (e.g., LocU1, . . . , LocUm) of respective mobile users (e.g.,U1, U2, . . . , Um) using their respective personal mobile devices(e.g., 315, . . . , 31 m) carried and/or worn by the users as the userstraverse various areas including those serviced by cellular telephonybase stations (e.g., cell towers 253 a′), serviced by GPS satelliteconstellations 253 b′ (see also 271-274 of FIG. 2B) and serviced byfiner resolution, location determining means (e.g., scanners 327—seealso 127 of FIG. 1). When a user enters a coarsely monitored parkingapproach area (e.g., 160, 161 of FIG. 1) or finally stops in a curbsideor alike pickup spot (e.g., 141 a-141 n of FIGS. 1 and 241-244 of FIG.2B), the automatically and repeatedly output pushed GbLI-signals and/orthe request-driven pulled GbLI-signals are wirelessly received by anappropriate receiver and processed to determine, despite presence oferror in the GPS-based location report, the more likely locations of thevehicle (e.g., parking spot 242′). An attendant servicing the parkingareas is assigned to the corresponding transaction (e.g., deliveringthat recipient's food order) and is automatically informed of thesystem's best guess or guesses as to where the vehicle (e.g., 362) isparked based on the repeatedly output GbLI-signals.

As indicated in magnified details area 315′, the exemplary respectivemobile device 315 of exemplary user U1 typically has a predeterminedoperating system (OS) 313 currently executing within it. Device 315 mayhave a set of application program-to-OS interfaces (APIs) 314 a forallowing various further programs 317 within the device 315 to accessresources of the OS 313. In one embodiment, the OS allows for OSmediated control over local telephony resources 314 b, Wi-Fi interfaceresources 314 c (e.g., including generation G4 resources), Bluetooth™resources 314 d, and GPS resources 314 e. One of the API accessibleresources of the OS is that for establishing one or more backgroundperipheral services (BPS's) 318 that may be dynamically and wirelesslyconnected to from external devices (e.g., scanners 127 a-127 n). Theexecuting OS 313 may on its own periodically test for presence of nearbyBluetooth™ and/or Wi-Fi devices (e.g., scanners 127 a-127 n, 5G Wi-Firouters 129 a,129 b and alike other such short range transceivers, e.g.,UWB ones) and in response to detected presence, occasionally wirelesslybroadcast its own Bluetooth™ beacon and/or repeated Wi-Fi signal orother presence-reporting signal which includes a current hardwareaccessing code (HAC) of the mobile device 315. In one embodiment, theBluetooth™ reporting signal has a unique and consistent signatureportion that can be used for locating the HAC code as being positionedat a predetermined bit position of fixed bit distance away from a uniquesignature portion of the PA-signal. The HAC code may extracted based onits predetermined bit position relative to the signature even though theHAC code itself changes on a pseudorandom basis. The schematic of FIG. 3illustrates the code for occasionally transmitting a rotating HAC asbeing disposed at section 319 of the personal mobile device. Theschematic of FIG. 3 also depicts one or more of established BPS's atarea 318. One of the BPSs is one which transmits automatically repeatedGbLI-signals that include respective GPS-based reported locationinformation and unique identification information that uniquelyidentifies at least one of the GPS position reporting device, the ownerof the device and/or a transaction currently associated with the deviceor its owner. In one embodiment, the identification information includesan associated TID (a system-assigned temporary transaction ID sequence).The received GbLI-signals can be routed out via the internet to a server(e.g., 340 b) controlled by the establishment. Another of the BPSs isone which transmits a Wi-Fi signal revealing the current cellulartelephony coordinates of the mobile device 315 as well as identifyingthe mobile device (e.g., by its currently assigned TID). This Wi-Fisignal can be routed out via the internet to a server (e.g., 340 b)controlled by the establishment. The establishment controlled server(e.g., 340 b) may then determine current coarse locations of the mobiledevice based on the received GPS and/or telephony information and storethe results in corresponding database entries (e.g., 34 m.5). The coarseposition determinations may be refined, for example using the polygonalareas overlap methods disclosed herein in cases where it is reasonableto assume that the vehicle has parked or otherwise stopped in apre-mapped polygonal area.

Various foreground programs that may be used by the user while waitingfor provisioning of the requested goods and/or services are depicted asbeing present in area 317. API's to the local apps in the mobile deviceare depicted as being present in area 314 f. One of the foregroundprograms that will be running in region 317 in accordance with oneembodiment is a vendor's ordering and order progress advisement program.An example of an initial, program launching GUI for the mobile device isdepicted at 315 with application invoking icons such as 311 and 312being present on the displayed graphical user interface. One of theapplication invoking icons (e.g., 311 or 312) may cause a launching of avendor's ordering and order progress advisement application. Thisapplication is stored in area 317 after being downloaded for examplefrom a vendor controlled server 340 a located in cloud 330 or elsewhereon the Internet 320. The ordering and order progress advisementapplication may advise the waiting user that an attendant is on his/herway to the parking spot with the requested goods/services and shouldarrive in X minutes. The application may give the user a generaldescription of the attendant (e.g., what kind of uniform is being worn)for purpose of improved security.

FIG. 3 more broadly depicts an integrated client-server/internet/cloudsystem 300 (or more generically, an integrated multi-device system 300)within which the here disclosed technology may be implemented. System300 may be understood as being an automated machine system havingdistributed intelligent resources including a variety ofdifferently-located data processing and data communication mechanismsincluding for example, user-carried/worn mobile wireless units (e.g.,wireless smartphones 315, . . . , 31 m) configured to allow end-usersthereof (e.g., U1, U2 . . . Um) to request from respective end-useroccupied locations (e.g., LocU1) services from differently locatedenterprise hosts (e.g., on-internet 320 and/or in-cloud servers 340 a,340 b, etc.). In one embodiment, server 340 a handles the downloading ofvendor ordering and order progress advisement apps into mobile devicesthat request them. The downloading process may include generating uniquecustomer profiles (e.g., including billing information) and customeridentifications that are to be used when the respective customers placeorders at a later time. In one embodiment server 340 b handles themanaging of placed orders. Server 340 b may include or connect to anorder management database which keeps track for each order-placing user(e.g., user Um, where m is an integer) of: (a) the user's customerprofile 34 m.1, (b) the details of the placed order 34 m.2; (c) asystem-assigned temporary and unique transaction identification sequence(TID) 34 m.3 assigned to the corresponding transaction; (d) a currenthardware address (e.g., HAC) being currently used by the customer'spersonal mobile device (e.g., 31 m.4); (e) a current one or more coarseand comparatively more pinpointed locations 34 m.5 of where therecipient is determined to most likely be present at (e.g., in or nearthe establishment or further away and including those determined to highlevel of confidence using directional antennas); (f) information aboutthe delivery status 34 m.6 of the requested goods and/or services thatthe present transaction is directed to; and optionally additionalinformation as may be appropriate for the vendor's business model.

It is to be understood that the illustrated configuration of system 300is merely exemplary. As indicated, it comprises at least a few, but moretypically a very large number (e.g., thousands) of end-user devices 315(only a few shown in the form of wireless smartphones but understood torepresent many similarly situated mobile and/or stationary clientmachines—including the smartphone wireless client kinds, smart watches,and cable-connected desktop kinds). These end-user devices 315 arecapable of originating service requests which are ultimately forwardedto service-providing host machines (e.g., in-cloud servers like 340 b)within a cloud environment 330 or otherwise on-internet or linked-tointernet machines (e.g., 340 b). Results from the service-providing hostmachines are thereafter typically returned to the end-user devices (315,. . . 31 m) and displayed or otherwise communicated to the end-users(e.g., U1, U2, . . . , Um, m being an integer). For example, if thebusiness of the vendor is an online, food pre-ordering one, the end-user(U1) may have installed on his/her smartphone (315) a softwareapplication (“app” 317) that automatically requests from the ordermanaging server 340 b, a list of nearest vendor venue locations, themenu of the items that may be ordered online and estimates for when theitems will be ready for pick up at a selected one of the venues. Inresponse to the request, enterprise software and hardware modulesautomatically identify the user, pull up a user profile (e.g., 34 m.1),store the order details (34 m.2), assign a temporary and uniquetransaction identification sequence (TID) 34 m.3 to the correspondingtransaction (install it into a corresponding one or more BPSs) andinform the customer of a time range when he or she might arrive at thevenue to pick up the order as well a specific location for the pickup(e.g., a drive-through window with directional antenna detection of theuser being directly in front of the window). The assigned TID may bedownloaded into the BPS's of the ordering app at that time orderplacement or at a later time before it is needed.

When the customer (e.g., Um) arrives at the designated venue and entersan area covered by the receivers (not specifically shown, but coupled tosignal detectors like antennas 127 n for example) that are configured todetect the GbLI-signals associated with the venue (e.g., 100), at leastone of the local (e.g., 336) or backend (e.g., 340 b) servers startsfollowing the trajectory of the customer's vehicle (e.g., 362) as itmoves down the lanes of the lot looking for a usable parking spot orother pickup spot. Then when the vehicle's velocity is at or below apredetermined minimum that indicates it is parking (or finishing itsparking maneuvers), the above described GPS-based position reporting andposition determination refinement steps are undertaken. In oneembodiment and briefly, the presence of a HAC-advertising mobile deviceis detected; an attempt is made to dynamically connect wirelessly to theTID-returning and GPS-reported location returning BPS of that mobiledevice. The collected information is used for deciding which specificparking spot the mobile device is located inside of. In one embodiment,the local server 336 then consults with a database or expert rulesknowledge base or an artificial intelligence (AI) system that had beenappropriately pre-trained to collect rules and/or supplementalinformation for more accurately determining the most likely one or morelocations for the targeted customer in view of other factors (e.g.,background noise, radio reflections, etc.) that may be currently presentat the venue. The determined one or more locations are then relayed tothe tracking database, for example into entries region 34 m.5. Theentries region 34 m.5 may store a history of recent locations andprediction of where the tracked user is most likely to be next located.A human or robotic server for assisting in quick delivery of therequested goods and/or services may then be dispatched to the predictedlocation of the customer.

In one embodiment, signal coupling from each of finer resolutionscanners (127 a-127 n) to the local server 336 is a wireless one such asconducted over a Wi-Fi network. Alternatively, Bluetooth™ signals or UWBsignals may be used where one scanner (e.g., 127 a) relays itsdetections and measurements to the next adjacent scanner (e.g., 127 b)and so on until the collected detection and measurement reports arerelayed to the local server 336. Signals coupling link 335 representsthe various ways in which the respective detections and measurements ofthe scanners (127 a-127 n) and or GPS position reporting devices arerelayed to the local server 336. The signals coupling link 335 may be awired one and/or may include wired and wireless subportions as opposedto being an all wireless signals coupling link.

In one embodiment, after receiving the respective detections andconducting the locations determinations as well as associating them withthe detected TID sequence, the local server 336 connects via theInternet 320 to the order management server 340 b. The order managementserver 340 b uses the relayed TID sequence to reference thecorresponding customer order details 34 m.1-34 m.7 of user Um andhis/her corresponding order. The order management server 340 b mayadditionally consult with an expert knowledge base and/or installed AI356 (example shown in server 340′) to determine, based on the relayedsignal measurements and location determinations, what the one or moremost likely current locations of the customer are at the respectivevenue and for the extant conditions there. When the ordered goods and/orservices are ready for delivery to or pickup by the customer, the ordermanagement server 340 b reports the latest one or more most likelylocations of the establishment. For example the report may be in theform of a sorted list of most to least likely locations. In oneembodiment, after pickup or delivery is reported as complete, thecorresponding TID is erased from the user's mobile device and also fromthe database storage locations (e.g., 34 m.3) so as to preserve privacy.

Aside from the end-user devices (e.g., 315, . . . , 31 m) and the cloudservers (e.g., 340 b) the system 300 comprises: one or more wired and/orwireless communication fabrics 316, 325, 335 (shown in the form ofbidirectional interconnects) intercoupling the end-user client devices(e.g., 315, . . . , 31 m) with the various networked servers (e.g., 336,340 a, 340 b, 340′).

Still referring to FIG. 3, a further walk through is provided here withrespect to detailed components that may be found in one or more of themobile devices and/or respective servers. Item 311 represents a firstuser-activateable software application (first mobile app) that may belaunched from within the exemplary mobile client 315 (e.g., asmartphone, but could instead be a tablet, a laptop, a wearablecomputing device; i.e. smartwatch or other). Item 312 represents asecond such user-activateable software application (second mobile app)and generally there are many more. Each end-user installed application(e.g., 311, 312) can come in the form of nontransiently recorded digitalcode (i.e. object code or source code) that is defined and stored in amemory for instructing a target class of data processing units toperform in accordance with end-user-side defined application programs(‘mobile apps’ for short) as well as to cooperate with server sideapplications implemented on the other side of communications links 316,325, etc. In one embodiment and the case where an order is placed forrespective goods and/or services by way of a non-mobile or not normallyused client machine (e.g., a desktop computer), the order managementserver 340 b automatically recognizes this condition and uses dataavailable in the customer's profile 34 m.1 to access the user's normallycarried, personal mobile device and to transfer the assigned TID to thatnormally carried personal mobile device. In this instance, it isunderstood that appropriate, vendor provided software has been preloadedinto the normally carried personal mobile device for securely enablingsuch transfer of the TID to the targeted mobile device. In this way,even if the customer places the order by way of a home desktop computerand then arrives at the venue with his/her normally-used mobile device,the customer tracking subsystem will still work.

More generally, each app (e.g., 311, 312, 317) may come from a differentbusiness or other enterprise and may require the assistance of variousand different online resources (e.g., Internet, Intranet and/or cloudcomputing resources). Each enterprise may be responsible for maintainingin good operating order its portions of the system (e.g., localscanners, local servers, Internet, Intranet and/or cloud computingresources). Accordingly, the system 300 is shown as including in atleast one server 340′, an expert knowledge base 356 which containsvarious kinds of different expert rules for handling differentconditions. One set of expert rules may provide for optimized customerlocation determinations at a given venue or venue observable area 327.Another set of expert rules may provide for less than optimum butacceptable customer location pinpointing when background noise is high.Yet another set of expert rules may provide for variable locationdetermination based on different sets of layouts of furniture or otherstructural barriers at each respective venue and/or based on expectedradio interferences and/or reflections at the given venue. Yet other ofthe expert rules may relate to categorizing different types oftransactions and details about how to handle them, including how toresolve various problematic issues.

In addition to the AI system and/or expert knowledge base 356, one ormore other portions of the system 300 may contain interaction trackingresources 351 configured for tracking interactions between customers andrespective vendors and an interactions storing database 352 configuredfor storing and recalling the tracked interactions. Links 353 a (to afurther server 340 c), 353 b, 353 c and 353 d represent various ways inwhich the system resources may communicate one with the other.

As mentioned, block 340′ is representative of various resources that maybe found in client computers and/or the various servers. These resourcesmay include one or more local data processing units (e.g., CPU's 341),one or more local data storage units (e.g., RAM's 342, ROM's 343, Disks346), one or more local data communication units (e.g., COMM units 347),and a local backbone (e.g., local bus 345) that operatively couples themtogether as well as optionally coupling them to yet further ones oflocal resources 348. The other local resources 348 may include, but arenot limited to, specialized high speed graphics processing units (GPU's,not shown), specialized high speed digital signal processing units(DSPU's, not shown), custom programmable logic units (e.g., FPGA's, notshown), analog-to-digital interface units (A/D/A units, not shown),parallel data processing units (e.g., SIMD's, MIMD's, not shown), localuser interface terminals and so on.

It is to be understood that various ones of the merely exemplary andillustrated, “local” resource units (e.g., 341-348) may include or maybe differentiated into more refined kinds. For example, the local CPU's(only one shown as 341) may include single core, multicore andintegrated-with-GPU kinds. The local storage units (e.g., 342, 343, 346)may include high speed SRAM, DRAM kinds as well as configured forreprogrammable, nonvolatile solid state data storage (SSD) and/ormagnetic and/or other phase change kinds. The localcommunication-implementing units (only one shown as 347) may operativelycouple to various external data communicating links such as wired,wireless, long range, short range, serial, parallel, optical kindstypically operating in accordance with various ones of predeterminedcommunication protocols (e.g., internet transfer protocols, TCP/IP,Wi-Fi, Bluetooth™, UWB and so on). Similarly, the other local resources(only one shown as 348) may operatively couple to various externalelectromagnetic or other linkages 348 a and typically operate inaccordance with various ones of predetermined operating protocols.Additionally, various kinds of local software and/or firmware may beoperatively installed in one or more of the local storage units (e.g.,342, 343, 346) for execution by the local data processing units (e.g.,341) and for operative interaction with one another. The various kindsof local software and/or firmware may include different operatingsystems (OS's), various security features (e.g., firewalls), differentnetworking programs (e.g., web browsers), different application programs(e.g., product ordering, game playing, social media use, etc.) and soon.

The advantages of the present teachings over the art are numerous. It isto be understood that the present teachings are not to be limited tospecific disclosed embodiments. In the above description and for sake ofsimplicity, a fast food restaurant venue is described. However, thisdisclosure may be applied, but not limited to, valet parking or otherservices and/or curbside food/souvenir purchases made at stadiums,arenas, train stations, airports, big box store pickup areas and manyother venues where it is desirable to track and pinpoint (as best as canpractically be done) the location of a user of a normally carried and/orworn personal mobile device without encumbering the user to carry otherdevices not belonging to the user and/or not normally carried by theuser.

FIG. 4 illustrates a method 400 for determining location of a GbLISemitter in one of substantially adjacent polygonal areas (e.g.,pre-mapped parking spots). The method includes receiving respectiverequests (e.g., orders) for corresponding goods and/or services fromrespective patrons before they arrive at the venue (e.g., from homeonline) and/or when they are at a kiosk or ordering window location(e.g., 112 of FIG. 1) and associating the received orders with a uniqueidentification of a specific GbLIS emitter (e.g., 215′) possessed by thepatron. GbLI-signals output by the emitter are received and theGPS-based reported location information (e.g., 217) and uniqueidentification are extracted. The information is used to determine whento begin or when to expedite the preparing of the ordered goods and/orservices and/or when to begin or when to expedite their delivery to aspecific parking spot (a specific one of substantially adjacent pluralpolygonal areas).

Entry for first-time use of the method 400 may occur at 405, whereaslater entry may occur at 415. In step 410 a user downloads into his/hernormally used personal mobile device (e.g., smartphone 215′, 315, or asmartwatch or another normally or routinely carried and/or worn personalwireless device) an order or request submitting and progress advisementapplication (app) that is configured for placing orders or requests toone or more prespecified vendors and/or vendor venues (e.g., fast foodestablishments, sit-down restaurants, big box store item pickup areas)and for then providing an identified recipient with progress informationsuch as when, where and how to receive the requested goods and/orservices. The downloaded app may include an installable backgroundperipheral service that causes the mobile device to periodically and/oron-demand output GbLI-signals when approaching the vendor premisesand/or while parked there or nearby (e.g., in an adjacent parking lot orgarage). It is within the contemplation of the present disclosure thatthe order or request submitting portion and the progress advisementportion are provided as two or more separate programs rather than onecombined app. The order or request submitting and progress advisementapplication (app) may be downloaded via the Internet and from one ormore vendor-specified websites. In one embodiment, the order or requestsubmitting app may first be downloaded into a desktop or laptop computerof a user and used for ordering where after the progress advisementportion is transferred into a personal mobile device (e.g., 315) of anidentified recipient (could be same as the order placer) for executionin that personal mobile device (e.g., 315).

In a subsequent step 420, the user launches the app as a foregroundexecuted process on his/her normally used personal mobile device (e.g.,smartphone 315) and, in one embodiment, uses the personal mobile deviceto order or request various goods and/or services for provisioning atone or more app-compatible vendor venues in accordance withorder-placing guidances provided by the app. Typically, the app willcooperate with an in-cloud server and obtain an identification of theuser and an identification of a time range in which the user expectsprovisioning of the ordered goods and/or services to occur. In analternate embodiment, the user places the order at the venue by way ofvenue-provided ordering mechanism (e.g., a drive-up microphone intowhich the user speaks). Once the ordering details are completed andassociated with the identification information to be include in theGbLI-signals to be output by the user's personal mobile device, the appalso establishes within the user's personal mobile device one or morebackground peripheral services (BPSs) which may be dynamically connectedto by external devices (e.g., the scanners 127 a-127 n at or near thevendor's venue). When one of these established BPSs is connected to, andit temporarily awakens, transmits a GbLI-signal containing theidentification that has been assigned to the order and then goes back tosleep. The temporarily awakened BPS does not block the user fromaccessing foreground applications or services on his/her personal mobiledevice and does not consume significant battery power. Steps 410 and 420may be carried out in the user's transport vehicle, home, office orelsewhere as convenient. They need not occur while the user is presentin the vendor's scanners-covered establishment. In one embodiment, asecond of the BPSs causes the user's mobile device to relay to a vendoraccessible server (e.g., 340 b), current location information of themobile device as determined based on connection to cellular telephonyequipment and/or GPS-based position determining equipment.

In step 422 the user (U1) arrives at the establishment and enters ascanners-covered area of the vendor's establishment while carryinghis/her normally used mobile device (e.g., smart phone 215′). For oneclass of embodiments (e.g., Apple iPhones™) the operating system (OS) ofthe mobile device automatically detects presence of external Bluetooth™devices and occasionally broadcasts its own Bluetooth™ signals tothereby autonomously advertise its presence in the area and declare acurrent hardware address code (HAC) by way of which the personal devicemay be addressed. In one embodiment, the presence advertising operationis intercepted before broadcast and modified to include GPS-basedposition reporting information.

In step 424 an appropriate receiver (e.g., one of scanners 127 a-127 n)detects the reported GbLI-signals and checks a local database todetermine if the included customer and/or transaction identification isalready recorded in an area of the database so that additional customerand/or transaction related information can be pulled up.

Also in step 424, the detected GbLI-signals are used to track thetrajectory of the user's vehicle (e.g., 114″) as it approaches (e.g., onpublic street 160) a corresponding pickup spot or parking lot, garage orother like structure 140. The collected information is sored in adatabase and analyzed to determine if order preparation and/or deliveryneeds to be begun or expedited.

Step 430 represents the use of one or more GPS-based reported locationsand the respective CoMPTL's circle or other geometric figure boundingthe MPTL's for those reported locations to thereby determine whatparking spots or other pre-mapped polygonal areas the vehicle is movingadjacent to and to thereby estimate the vehicle's current trajectory andspeed. The determined presence and trajectory of the user's personalmobile device are reported to an order management system. In oneembodiment, the order management system tracks movements of the userand/or his/her personal mobile device so as to determine whether theuser has settled at a relatively stable parking spot (e.g., 242′) andwhat the coordinates of that location are or whether the user isadvancing towards such a quick pickup spot. The order management systemmay then dispatch instructions to appropriate human and/or roboticservice providers to advance or delay the preparation and/or productionof the requested goods and/or services so that provisioning of thegoods/services timely intersects with the determined or predictedlocation of the recipient in accordance with a current provisioningplan.

Step 431 determines whether the customer's vehicle has finally stoppedin a parking spot or other pickup spot or whether the customer is stilldriving around looking for such a spot. If still driving, controlreturns to step 430. If speed drops below a predetermined minimum formore than a predetermined time duration, control advance to step 432.

In step 440, the final parking spot or other pickup spot is determinedusing the overlap scores developed from the overlaying of a mapping ofthe reported location onto a mapping of the potential pickup/parkingspots and the generation of the CoMPTL's circle or other suchMPTL's-bounding figure on the mapping. A number of different algorithmsmay be used for determining the best one or best handful of answers. Onesuch algorithm is depicted in FIG. 5. In one embodiment, priortrajectory data is used to refine the answer or answer as indicated instep 441. While the parked customer is waiting for delivery of theordered goods/services to the determined parking spot or other pickupspot, the customer may access and use one or more foreground apps and/orservices of his/her personal mobile device (as indicated by steps450-460) while not interfering with the occasional and temporaryreawakening's of the GbLIS-transmitting background peripheral service(BPS). The user-accessible one or more foreground apps and/or servicesmay include games, web browsers, email applications, social mediaapplications and so forth. The user therefore can be entertained or mayconduct work tasks while waiting for delivery of the requested goodsand/or services. One of the foreground apps and/or services may be theorder progress advisement app which advises the user about the progressof, and/or currently planned time, location and method of providing therequested goods and/or services to that user.

Step 442 represents the relaying of the decision(s) about the parkedspot of the customer to one or more attendants (e.g., those in area 132)of the establishment who are assigned to deliver ordered goods/servicesto their respective destinations. As mentioned, while the customer waitsfor the delivery (completed at step 461), the customer may use his/hermobile device 215′ for other purposes.

In one embodiment, after the requested goods and/or services have beensatisfactorily provided to the tracked user (step 461), the usertracking BPSs are automatically deleted from the user's personal mobiledevice and the transaction TID is automatically deleted from the localdatabase. This deletion step assures that the BPSs and TID are ephemeralobjects which disappear after the order has been fulfilled. As a result,the user's privacy is secured in that the details of the delivered ordercan no longer be found using the temporarily assigned TID.

FIG. 5 depicts a machine-implemented algorithm 500 for selectivelychoosing the polygonal area that most likely contains the GPS positionreporting device (e.g., 215′). Entry is made at 505 with a respectivedevice ID and reported location (e.g., 217) already in hand. At step 510it is determined whether pre-parking monitoring should be conducted. Theanswer may depend on current processor workload and available dataprocessing bandwidth. If no, control is advanced to step 520.

If the answer to test step 510 is yes, control advances to a pre-parksubroutine that includes step 515. The pre-park subroutine (not allshown) automatically repeatedly extracts the velocity reports from thereceived GbLI-signals as well as the GPS-based location reports. A roughestimate of the vehicle's trajectory is determined and stored. In step515 it is determined whether the vehicle has reached its intendedpolygonal area (e.g., parking spot) and is either in the final act ofcautiously parking or it has already stopped an is parked. The testingincludes determining if current speed is below a predetermined minimumor is at zero for more than a negligible pause time. The predeterminedminimum can be 5 MPH or less. The pause time P can be in the range ofabout 5 to 15 seconds. Pause time P may be a function of context, forexample how long the typical stop time is at lane crossings and howcrowded is the parking lot, garage or other like structure (e.g., 140).If the answer to test step 510 is Yes, control returns to step 510.

On the other hand, if the answer to test step 510 is No, (speed is notgreater than the minimum or zero) control advances to a first filteringroutine. In step 510 it is determined whether the first filteringroutine is enabled or not. The answer may be a function of context, forexample, how much ambient noise is present such that the first filteringis desirable. If no filtering is warranted, the overlap scores for alloverlapped polygonal areas are computed and kept (stored into to besorted list) in step 522.

On the other hand, if the answer to test step 520 is Yes (the firstfiltering is desired), control advances to step 525 in which therespective computed overlap scores for the respective overlappedpolygonal areas are tested for quality Q before being kept. In oneembodiment, the quality Q test requires that the overlap score be abovea predetermined fraction (e.g., 25%) of the maximum overlap scoreobtainable for that polygonal area. In the same or another embodiment,the quality Q test requires that the overlap area be above apredetermined fraction (e.g., 20%-40%) of the total area of theoverlapped polygonal area. Overlaps that fail the quality Q test arediscarded rather than being appended to the tail of a sortable list(e.g., an array having polygonal area identifying slots A(0), A(1),A(2), etc. and associated overlap score slots OS(0), OS(1), OS(2),etc.).

In step 530 the completed list of overlapped area identifications andassociated overlap score are sorted according to overlap score with thehighest score being in slot OS(0) and the identification of thepolygonal area receiving the highest score being in slot A(0). If thenumber of overlap scores is just one due to the first quality test, step535 advances control to step 541.

On the other hand, if it is determined in step 535 that the array isempty (no qualifying overlap areas or qualifying overlap scores werefound), control advance to step 542.

As a third option, if more than 1 overlapped area remain in the array, afurther quality test is conducted in steps 537-538. In step 537, adistinction factor (e.g., Z) is determined as a function of the ratio ofthe overlap score for the area identified in the top slot A(0) and theoverlap score for the area identified in the next lower slot A(1). Teststep 538 compares the distinction factor against a predetermineddistinction requirement R. In one embodiment, R is in the range of about1.5 to 2.5 and test step 538 requires that Z be greater than R. If nottrue (No), control advances to step 542 in which it is reported thatcurrently it is indeterminable as to which polygonal area (e.g., parkingspot) contains the subject GPS position reporting device (e.g., 215′).An exit is made at step 506.

On the other hand, if test step 538 determines that the distinctionrequirement R is met (Yes) control advances to step 541 in which it isreported that currently the polygonal area (e.g., parking spot)identified in slot A(0) contains the subject GPS position reportingdevice (e.g., 215′). An exit is made at step 508.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented using one or more hardwarecomputer systems that execute software programs. Further, in anexemplary, non-limited embodiment, implementations can includedistributed processing, component/object distributed processing, andparallel processing. Virtual computer system processing can beconstructed to implement one or more of the methods or functionalitiesas described herein, and a processor described herein may be used tosupport a virtual processing environment.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatuses(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a digital processor of a digital programmable computer orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable instruction execution apparatus, create amechanism for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. All instructions need not beexecuted a by same one processor and can instead be distributed among aplurality of operatively cooperative processors. The terminology, ‘atleast one processor’ as used herein is to be understood as covering bothoptions, namely having one processor execute the all instructions ordistributing the instructions for execution by two or more processors.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The description of the present disclosure has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

For purposes of this document, each process associated with thedisclosed technology may be performed continuously or on an interruptedmulti-tasking basis and by one or more computing devices. Each step in aprocess may be performed by the same or different computing devices asthose used in other steps, and each step need not necessarily beperformed by a single computing device.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claimed subject matter.

What is claimed is:
 1. A method of providing improved GPS-baseddetermination that a specific GPS-based position reporting device islocated in a specific one of substantially adjacent polygonal areas, themethod comprising: receiving a GPS-based Location-reporting andID-reporting Signal (GbLIS) from a specific GPS-based position reportingdevice and extracting a respective GPS-based reported location from thereceived GbLIS; positioning a mapping of the reported location on apre-established mapping of the substantially adjacent polygonal areasand generating from the on-map positioning of the reported location, anon-map bounded geometric figure that encompasses the mapped reportedlocation and that bounds on the mapping, corresponding predetermined asmore probable true locations (MPTL's) among possible locations for theposition reporting device; determining respective overlaps of the on-mapbounded geometric figure with respective ones of the mapped polygonalareas; using respective overlap areas of the on-map bounded geometricfigure with respective ones of the mapped polygonal areas whileexcluding non-overlap areas to generate respective overlap scoresindicative of which of the mapped polygonal areas are likely toencompass more of the MPTL's than others of the mapped polygonal areas;identifying a subset of the mapped polygonal areas that havesignificantly higher overlap scores than others of the substantiallyadjacent polygonal areas; and reporting part or all of the identifiedsubset as constituting likely candidates for where the reporting deviceis located.
 2. The method of claim 1, wherein: the identifying of thesubset of the mapped polygonal areas that have significantly higheroverlap scores includes applying a filter to the generated respectiveoverlap scores to thereby eliminate from further consideration, those ofthe generated overlap scores that do not satisfy predeterminedsignificance criteria.
 3. The method of claim 2, wherein: thepredetermined significance criteria includes requiring the respectiveoverlap score to be equal to or greater than a predetermined fraction ofa maximum overlap score that can be generated for the respectivepolygonal area.
 4. The method of claim 2, wherein: the predeterminedsignificance criteria includes requiring the respective overlap area tobe equal to or greater than a predetermined fraction of the area of therespective polygonal area.
 5. The method of claim 2, wherein: thepredetermined significance criteria includes requiring the respectiveoverlap score of a candidate polygonal area to be at least 50% greaterthan that of another polygonal area.
 6. The method of claim 1, wherein:the bounding geometric figure is a circle (CoMPTL's).
 7. The method ofclaim 1, wherein: the predetermined as more probable true locations(MPTL's) encompassed by the bounding geometric figure are constituted bymore than 50% of more frequent true locations found during samplingsthat produce a representative same reported location and the lessfrequent true locations are excluded.
 8. The method of claim 1, wherein:the predetermined as more probable true locations (MPTL's) encompassedby the bounding geometric figure are constituted by at least 68% of morefrequent true locations found during samplings that produce arepresentative same reported location and the less frequent truelocations are excluded.
 9. The method of claim 1, wherein: thepredetermined as more probable true locations (MPTL's) encompassed bythe bounding geometric figure are constituted by at least 95% of morefrequent true locations found during samplings that produce arepresentative same reported location and the less frequent truelocations are excluded.
 10. The method of claim 1, wherein: the boundinggeometric figure is divided into subdivisions each having a respectiveweight Wi assigned to it; and the generating of respective overlapscores for each of the mapped polygonal areas includes determiningrespective overlap by overlapping ones of the subdivisions and using therespective weights Wi assigned to the overlapping subdivisions todetermine the respective overlap score for each of the mapped polygonalareas.
 11. The method of claim 10, wherein: the subdivisions include anannular subdivision.
 12. The method of claim 1, wherein: the reportingincludes wireless transmitting the part or all of the identified subsetto a communication device accessible by an attendant who is assigned toservice the polygonal area occupied by the specific GPS-based positionreporting device.
 13. The method of claim 12, wherein: the polygonalareas include two or more of parking spots or curbside pickup locations.14. The method of claim 13, wherein: the polygonal areas include two ormore parking spots disposed in a parking lot and/or garage.
 15. Themethod of claim 12, wherein: the attendant who is assigned to servicethe polygonal area occupied by the specific GPS-based position reportingdevice is tasked with delivering to that polygonal area, pre-specifiedgoods within a pre-specified time duration.
 16. The method of claim 2and further comprising: before said determining of the respectiveoverlaps of the on-map bounded geometric figure with respective ones ofthe mapped polygonal areas, testing for and verifying that a speed ofthe position reporting device is less than predetermined minimum. 17.The method of claim 16 wherein: the tested for speed is indicated byinformation included in the received GbLIS.
 18. The method of claim 16wherein: the tested for speed is indicated by information provided byaccelerometer disposed within the position reporting device.
 19. Themethod of claim 2 wherein: the applying of the filter to the generatedrespective overlap scores includes comparing ratios of respectiveoverlap scores of respective ones of the mapped polygonal areas that areindicated to likely encompass more of the MPTL's than others of themapped polygonal areas.
 20. A system configured for assisting indelivery of pre-specified goods and/or services to a polygonal areaoccupied by a specific GPS-based position reporting device where thespecific GPS-based position reporting device automatically repeatedlytransmits GPS-based Location-reporting and ID-reporting Signals(GbLI-signals) containing respective GPS-based reported locations andspecific identifications of the specific GPS-based position reportingdevice and/or of a specific user associated with the reporting deviceand/or of a specific transaction associated with the reporting device,the system comprising: a receiver configured to wirelessly receive thetransmitted GbLI-signals; a digital storage configured to store therein,respective digital representations of the GPS-based reported locationsand specific identifications obtained from the received GbLI-signals; amapper having stored therein a mapping of substantially adjacentpolygonal areas including the polygonal area occupied by the specificGPS-based position reporting device, the mapper being configured to mapone of the respective digital representations of the GPS-based reportedlocations onto the mapping and to generate therefrom a an on-map boundedgeometric figure that encompasses the mapped reported location and thatbounds on the mapping, corresponding predetermined as more probable truelocations (MPTL's) among possible locations for the position reportingdevice while excluding predetermined as less probable ones among thepossible locations; an overlap scores generator, operatively coupled tothe mapper and configured to generate overlap scores respectivelyassociated with overlaps of the on-map bounded geometric figure with theon-map representations of the substantially adjacent polygonal areas; asorter, operatively coupled to the overlap scores generator andconfigured to sort the generated overlap scores according to value; anda reporter, operatively coupled to the sorter and configured to output areport that identifies a highest scoring one or a highest scoring subsetof the polygonal areas.
 21. The system of claim 20 and furthercomprising: a filter operatively coupled to the overlap scores generatorand configured to remove from the generated overlap scores those of thescores that do not meet pre-specified quality criteria.
 22. The systemof claim 20 wherein the corresponding predetermined as more probabletrue locations (MPTL's) are constituted by at least a more probable 68%of the corresponding predetermined as more probable true locations. 23.A computer system comprising one or more processors and a memory havingcollectively stored therein instructions that, when executed by the oneor more processors, cause the one or more processors to execute aprocess that assists in identification among a plurality ofsubstantially adjacent polygonal areas, of a specific polygonal areaoccupied by a specific GPS-based position reporting device where thespecific GPS-based position reporting device automatically repeatedlytransmits GPS-based Location-reporting and ID-reporting Signals(GbLI-signals) containing respective GPS-based reported locations andspecific identifications of the specific GPS-based position reportingdevice and/or of a specific user associated with the reporting deviceand/or of a specific transaction associated with the reporting device,the executed process comprising: obtaining a respective GPS-basedreported location extracted from a received GbLIS of the specificGPS-based position reporting device; positioning a mapping of thereported location on a pre-established mapping of the plurality ofsubstantially adjacent polygonal areas and generating from the on-mappositioning of the reported location, an on-map bounded geometric figurethat encompasses the mapped reported location and that bounds on themapping, corresponding predetermined as more probable true locations(MPTL's) among possible locations for the position reporting device;determining respective overlaps of the on-map bounded geometric figurewith respective ones of the mapped polygonal areas; using respectiveoverlap areas of the on-map bounded geometric figure with respectiveones of the mapped polygonal areas while excluding non-overlap areas togenerate respective overlap scores indicative of which of the mappedpolygonal areas are likely to encompass more of the MPTL's than othersof the mapped polygonal areas; identifying a subset of the mappedpolygonal areas that have significantly higher overlap scores thanothers of the substantially adjacent polygonal areas; and reporting partor all of the identified subset as constituting likely candidates forwhere the reporting device is located.
 24. The system of claim 23wherein; the corresponding predetermined as more probable true locations(MPTL's) are constituted by at least a more probable 68% of thecorresponding predetermined as more probable true locations.